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PDQ is a library of functions that helps you to express and solve performance questions about computer systems using the abstraction of queues. The queueing paradigm is a natural choice because, whether big (a web site) or small (a laptop), all computer systems can be represented as a network or circuit of buffers and a buffer is a...

In a recent Guerrilla CaP Group discussion, Baron S. wrote:....BS> Using gnuplot against the dataset I gave, I get BS> sigma 0.0207163 +/- 0.001323 (6.385%) BS> kappa 0.000861226 +/- 5.414e-05 (6.287%) The Gnuplot output includes the errors for each of the universal scalability law (USL) coefficients. A question about the magnitude of...

In the recent GDAT class, confidence intervals (CI) for performance data were discussed. Their generalization to confidence bands (CB) for scalability projections using the USL model also came up informally. I showed a prototype plot but it was an ugly hack. Later requests from GDAT attendees to apply CBs to their own data meant I had...

Once you've downloaded PDQ with a view to solving your performance-related questions, the next step is getting started using it. Why not have some fun with blocks? Fun-ctional blocks, that is. Since all digital computers and network systems can be considered as a collection of functional blocks and these blocks often contain buffers, their performance can be modeled...

Although I use Excel all the time, and I strongly encourage my students to use it for performance analysis and CaP, I was forced to include a warranty disclaimer in my GCaP book because I discovered a serious numerical error while writing Appendix B. There, my intention was just to show that Excel gives essentially the same results...

Here's a summary of some things we learnt about applying R to computer performance and capacity planning data in the GDAT Class last week.
Neural nets pkg nnet applied to CPU performance data in the Ripley and Venables book (see Section 8.10).
How to do stacked plots that Jim calls "spark plots."
Jim told...

Only one month to go! Register now for the Guerrilla Data Analysis Techniques (GDAT) class to be held during the week of August 9-13, 2010. The focus will be on using R and the PDQ-R for computer performance analysis and capacity planning.(Click on t...

Having finally popped the stack on computing prime numbers with R in Part II and Part III, we are now in a position to discuss their relevance for computational scalability.My original intent was to show how poor partitioning of a workload can defeat the linear scalability expected when full parallelism is otherwise attainable, i.e., zero...

Here is a scatter plot with the coordinate labels deliberately omitted.
Figure 1.
Do you see any trends? How would you model these data? It just so happens that this scatterplot is arguably the most famous scatterplot in history. One aficionado, writing more than forty years after its publication, commented skeptically :" data points were consequently spread...

Popping Part III off the stack—where I ended up unexpectedly discovering that the primes and primlist functions are broken in the schoolmath package on CRAN—let's see what prime numbers look like when computed correctly in R. To do this, I've had to roll my own prime number generating function.Personalizing primes in RFor what I want...